Context-aware Object Typicality Measurement in Fuzzy Ontology

  • Yi Cai
  • Ching-man Au Yeung
  • Ho-fung Leung
Chapter

Abstract

In Chapter 6, we proposed a better ontology model which can overcome the limitations of our first model. However, psychologists find that context is important in measuring object typicality [1]. In this chapter, we present a formal model of context-aware ontology with multi-prototype concept and object typicality based on studies of cognitive psychology. It can be used to formalize object typicality in context-aware ontology, which is a capability not featured in previous models.

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References

  1. [1]
    Murphy GL (2002) The Big Book of Concepts. MIT Press, Boston.Google Scholar
  2. [2]
    Ozturk P, Aamodt A (1997) Towards a Model of Context for Case-based Diagnostic Problem Solving. In: Context’ 99, pp 198–208.Google Scholar
  3. [3]
    Barsalou LW (1985) Ideals, Central Tendency, and Frequency of Instantiation as Determinants of Graded Structure in Categories. J Exp Psychol Learn Mem Cogn 11(4): 629–654.CrossRefGoogle Scholar
  4. [4]
    Santini S, Jain R (1995) Similarity Matching. In: ACCV, pp 571-580. https: citeseer.ist.psu.edu/163013.html. Accessed 18 May 2011.Google Scholar
  5. [5]
    Lesot MJ, Mouillet L, Meunier BB (2005) Fuzzy Prototypes Based on Typicality Degrees. In: Proceedings of the 8th Fuzzy Days’ 04, Springer, Heidelberg.Google Scholar
  6. [6]
    Yager RR (1988) On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decisionmaking. IEEE Trans on Systems, Man, and Cybernetics 18(1): 183–190.MathSciNetMATHCrossRefGoogle Scholar
  7. [7]
    Cai Y, Leung HF (2008) Multi-prototype Concept and Object Typicality in Ontology. In: Proceedings of the 21st International Florida Artificial Intelligence Research Society Conference. AAAI Press, pp 470–475.Google Scholar
  8. [8]
    Gupta SK, Rao KS, Bhatnagar V (1999) K-means Clustering Algorithm for Categorical Attributes. In: DaWaK’ 99. Springer, London, pp 203–208.Google Scholar
  9. [9]
    Au Yeung CM, Leung HF (2006) Ontology with Likeliness and Typicality of Objects in Concepts. In: Proceedings of the 25th International Conference on Conceptual Modeling. Springer, vol 4215, pp 98–111.Google Scholar

Copyright information

© Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yi Cai
    • 1
  • Ching-man Au Yeung
    • 2
  • Ho-fung Leung
    • 3
  1. 1.School of Software EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Hong Kong Applied Science and Technology Research InstituteHong KongChina
  3. 3.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina

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